DREAM: Diffusion Rectification and Estimation-Adaptive Models
akhaliq
Abstract
We present DREAM, a novel training framework representing Diffusion Rectification and Estimation-Adaptive Models, requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models. DREAM features two components: diffusion rectification, which adjusts training to reflect the sampling process, and estimation adaptation, which balances perception against distortion. When applied to image super-resolution (SR), DREAM adeptly navigates the tradeoff between minimizing distortion and preserving high image quality. Experiments demonstrate DREAM's superiority over standard diffusion-based SR methods, showing a 2 to 3times faster training convergence and a 10 to 20times reduction in necessary sampling steps to achieve comparable or superior results. We hope DREAM will inspire a rethinking of diffusion model training paradigms.
Community
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach (2023)
- CoSeR: Bridging Image and Language for Cognitive Super-Resolution (2023)
- Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution (2023)
- EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution (2023)
- DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution (2023)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper